Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/38239
Title: Resampling-based ensemble methods for online class imbalance learning
Authors: Wang, Shuo
Minku, Leandro L.
Yao, Xin
First Published: 5-Aug-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: IEEE Transactions on Knowledge and Data Engineering, 2015, 27 (5), pp. 1356-1368
Abstract: Online class imbalance learning is a new learning problem that combines the challenges of both online learning and class imbalance learning. It deals with data streams having very skewed class distributions. This type of problems commonly exists in real-world applications, such as fault diagnosis of real-time control monitoring systems and intrusion detection in computer networks. In our earlier work, we defined class imbalance online, and proposed two learning algorithms OOB and UOB that build an ensemble model overcoming class imbalance in real time through resampling and time-decayed metrics. In this paper, we further improve the resampling strategy inside OOB and UOB, and look into their performance in both static and dynamic data streams. We give the first comprehensive analysis of class imbalance in data streams, in terms of data distributions, imbalance rates and changes in class imbalance status. We find that UOB is better at recognizing minority-class examples in static data streams, and OOB is more robust against dynamic changes in class imbalance status. The data distribution is a major factor affecting their performance. Based on the insight gained, we then propose two new ensemble methods that maintain both OOB and UOB with adaptive weights for final predictions, called WEOB1 and WEOB2. They are shown to possess the strength of OOB and UOB with good accuracy and robustness.
DOI Link: 10.1109/TKDE.2014.2345380
ISSN: 1041-4347
Links: http://ieeexplore.ieee.org/document/6871400/
http://hdl.handle.net/2381/38239
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2014. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Appears in Collections:Published Articles, Dept. of Computer Science

Files in This Item:
File Description SizeFormat 
06871400.pdfPublished (publisher PDF)792.7 kBAdobe PDFView/Open


Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.